Julep

Julep is a serverless backend platform for building production-grade AI agents and multi-step workflows. Rather than requiring developers to stitch together their own infrastructure for state management, memory, and task orchestration, Julep provides a managed system that handles these concerns out of the box.

At its core, Julep lets developers define agents and workflows using YAML or JSON — a declarative approach that keeps logic readable and version-controllable, much like infrastructure-as-code. Workflows can span multiple steps, branch conditionally, and coordinate between tools and LLMs without the developer needing to manage execution state manually. Sessions maintain conversation history between agents and users across interactions, making it practical to build assistants that remember context over time.

The platform connects to 50+ LLMs, meaning teams are not locked into a single model provider. Knowledge search, reasoning capabilities, and tool integrations are available as first-class features rather than bolted-on additions. Julep was selected for Meta's Llama Startup Program, signaling alignment with open-weight model ecosystems as well.

Where Julep differs from alternatives like LangChain, LlamaIndex, or CrewAI is in its infrastructure stance. Those frameworks run inside the developer's own application process and require the developer to manage deployment, persistence, and reliability. Julep is a managed service — the orchestration engine runs on their infrastructure, and developers interact with it via API. This is closer in spirit to platforms like Inngest or Temporal for workflow orchestration, but purpose-built for agentic AI workloads.

Compared to cloud-provider AI services (AWS Bedrock Agents, Google Vertex AI Agents), Julep is model-agnostic and less coupled to a specific cloud ecosystem, which gives teams more flexibility. The YAML/JSON workflow definition also tends to be more portable and auditable than proprietary visual builders.

The platform is positioned for teams that want to move from prototype to production without building orchestration infrastructure themselves. It handles session history, knowledge retrieval, and multi-step task execution as managed primitives. This makes it particularly well-suited for applications like customer support agents, research automation pipelines, document processing workflows, and any scenario where agents need to maintain state across multiple interactions or execute long-running tasks reliably.

Julep's API-first design means it can plug into existing backend architectures without dictating how the rest of the application is built. Developers define agent behavior through configuration rather than imperative code, which reduces boilerplate and makes workflows easier to inspect and modify. For teams that need observable, deterministic AI logic without the operational overhead of managing their own orchestration layer, Julep occupies a compelling position in the growing ecosystem of agentic AI infrastructure.